components/dataframe.pyi

"""gr.Dataframe() component"""

from __future__ import annotations

import warnings
from typing import (
    TYPE_CHECKING,
    Any,
    Callable,
    Dict,
    List,
    Literal,
    Optional,
    Tuple,
    Union,
)

import numpy as np
import semantic_version
from gradio_client.documentation import document

from gradio.components import Component
from gradio.data_classes import GradioModel
from gradio.events import Events

if TYPE_CHECKING:
    import pandas as pd
    import polars as pl  # type: ignore
    from pandas.io.formats.style import Styler


def _is_polars_available():
    import importlib.util

    spec = importlib.util.find_spec("polars")
    return bool(spec)


def _import_polars():
    import polars as pl  # type: ignore

    return pl


class DataframeData(GradioModel):
    headers: List[str]
    data: Union[List[List[Any]], List[Tuple[Any, ...]]]
    metadata: Optional[Dict[str, Optional[List[Any]]]] = None

from gradio.events import Dependency

@document()
class Dataframe(Component):
    """
    This component displays a table of value spreadsheet-like component. Can be used to display data as an output component, or as an input to collect data from the user.
    Demos: filter_records, matrix_transpose, tax_calculator, sort_records
    """

    EVENTS = [Events.change, Events.input, Events.select]

    data_model = DataframeData

    def __init__(
        self,
        value: pd.DataFrame
        | Styler
        | np.ndarray
        | pl.DataFrame
        | list
        | list[list]
        | dict
        | str
        | Callable
        | None = None,
        *,
        headers: list[str] | None = None,
        row_count: int | tuple[int, str] = (1, "dynamic"),
        col_count: int | tuple[int, str] | None = None,
        datatype: str | list[str] = "str",
        type: Literal["pandas", "numpy", "array", "polars"] = "pandas",
        latex_delimiters: list[dict[str, str | bool]] | None = None,
        label: str | None = None,
        show_label: bool | None = None,
        every: float | None = None,
        height: int = 500,
        scale: int | None = None,
        min_width: int = 160,
        interactive: bool | None = None,
        visible: bool = True,
        elem_id: str | None = None,
        elem_classes: list[str] | str | None = None,
        render: bool = True,
        key: int | str | None = None,
        wrap: bool = False,
        line_breaks: bool = True,
        column_widths: list[str | int] | None = None,
    ):
        """
        Parameters:
            value: Default value to display in the DataFrame. If a Styler is provided, it will be used to set the displayed value in the DataFrame (e.g. to set precision of numbers) if the `interactive` is False. If a Callable function is provided, the function will be called whenever the app loads to set the initial value of the component.
            headers: List of str header names. If None, no headers are shown.
            row_count: Limit number of rows for input and decide whether user can create new rows. The first element of the tuple is an `int`, the row count; the second should be 'fixed' or 'dynamic', the new row behaviour. If an `int` is passed the rows default to 'dynamic'
            col_count: Limit number of columns for input and decide whether user can create new columns. The first element of the tuple is an `int`, the number of columns; the second should be 'fixed' or 'dynamic', the new column behaviour. If an `int` is passed the columns default to 'dynamic'
            datatype: Datatype of values in sheet. Can be provided per column as a list of strings, or for the entire sheet as a single string. Valid datatypes are "str", "number", "bool", "date", and "markdown".
            type: Type of value to be returned by component. "pandas" for pandas dataframe, "numpy" for numpy array, "polars" for polars dataframe, or "array" for a Python list of lists.
            label: The label for this component. Appears above the component and is also used as the header if there are a table of examples for this component. If None and used in a `gr.Interface`, the label will be the name of the parameter this component is assigned to.
            latex_delimiters: A list of dicts of the form {"left": open delimiter (str), "right": close delimiter (str), "display": whether to display in newline (bool)} that will be used to render LaTeX expressions. If not provided, `latex_delimiters` is set to `[{ "left": "$$", "right": "$$", "display": True }]`, so only expressions enclosed in $$ delimiters will be rendered as LaTeX, and in a new line. Pass in an empty list to disable LaTeX rendering. For more information, see the [KaTeX documentation](https://katex.org/docs/autorender.html). Only applies to columns whose datatype is "markdown".
            label: The label for this component. Appears above the component and is also used as the header if there are a table of examples for this component. If None and used in a `gr.Interface`, the label will be the name of the parameter this component is assigned to.
            show_label: if True, will display label.
            every: If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute.
            height: The maximum height of the dataframe, specified in pixels if a number is passed, or in CSS units if a string is passed. If more rows are created than can fit in the height, a scrollbar will appear.
            scale: relative size compared to adjacent Components. For example if Components A and B are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide as B. Should be an integer. scale applies in Rows, and to top-level Components in Blocks where fill_height=True.
            min_width: minimum pixel width, will wrap if not sufficient screen space to satisfy this value. If a certain scale value results in this Component being narrower than min_width, the min_width parameter will be respected first.
            interactive: if True, will allow users to edit the dataframe; if False, can only be used to display data. If not provided, this is inferred based on whether the component is used as an input or output.
            visible: If False, component will be hidden.
            elem_id: An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
            elem_classes: An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.
            render: If False, component will not render be rendered in the Blocks context. Should be used if the intention is to assign event listeners now but render the component later.
            key: if assigned, will be used to assume identity across a re-render. Components that have the same key across a re-render will have their value preserved.
            wrap: If True, the text in table cells will wrap when appropriate. If False and the `column_width` parameter is not set, the column widths will expand based on the cell contents and the table may need to be horizontally scrolled. If `column_width` is set, then any overflow text will be hidden.
            line_breaks: If True (default), will enable Github-flavored Markdown line breaks in chatbot messages. If False, single new lines will be ignored. Only applies for columns of type "markdown."
            column_widths: An optional list representing the width of each column. The elements of the list should be in the format "100px" (ints are also accepted and converted to pixel values) or "10%". If not provided, the column widths will be automatically determined based on the content of the cells. Setting this parameter will cause the browser to try to fit the table within the page width.
        """
        self.wrap = wrap
        self.row_count = self.__process_counts(row_count)
        self.col_count = self.__process_counts(
            col_count, len(headers) if headers else 3
        )
        self.__validate_headers(headers, self.col_count[0])

        self.headers = (
            headers
            if headers is not None
            else [str(i) for i in (range(1, self.col_count[0] + 1))]
        )
        self.datatype = datatype
        valid_types = ["pandas", "numpy", "array", "polars"]
        if type not in valid_types:
            raise ValueError(
                f"Invalid value for parameter `type`: {type}. Please choose from one of: {valid_types}"
            )
        if type == "polars" and not _is_polars_available():
            raise ImportError(
                "Polars is not installed. Please install using `pip install polars`."
            )
        self.type = type
        values = {
            "str": "",
            "number": 0,
            "bool": False,
            "date": "01/01/1970",
            "markdown": "",
            "html": "",
        }
        column_dtypes = (
            [datatype] * self.col_count[0] if isinstance(datatype, str) else datatype
        )
        self.empty_input = {
            "headers": self.headers,
            "data": [
                [values[c] for c in column_dtypes] for _ in range(self.row_count[0])
            ],
            "metadata": None,
        }

        if latex_delimiters is None:
            latex_delimiters = [{"left": "$$", "right": "$$", "display": True}]
        self.latex_delimiters = latex_delimiters
        self.height = height
        self.line_breaks = line_breaks
        self.column_widths = [
            w if isinstance(w, str) else f"{w}px" for w in (column_widths or [])
        ]
        super().__init__(
            label=label,
            every=every,
            show_label=show_label,
            scale=scale,
            min_width=min_width,
            interactive=interactive,
            visible=visible,
            elem_id=elem_id,
            elem_classes=elem_classes,
            render=render,
            key=key,
            value=value,
        )

    def preprocess(
        self, payload: DataframeData
    ) -> pd.DataFrame | np.ndarray | pl.DataFrame | list[list]:
        """
        Parameters:
            payload: the uploaded spreadsheet data as an object with `headers` and `data` attributes
        Returns:
            Passes the uploaded spreadsheet data as a `pandas.DataFrame`, `numpy.array`, `polars.DataFrame`, or native 2D Python `list[list]` depending on `type`
        """
        import pandas as pd

        if self.type == "pandas":
            if payload.headers is not None:
                return pd.DataFrame(
                    [] if payload.data == [[]] else payload.data,
                    columns=payload.headers,
                )
            else:
                return pd.DataFrame(payload.data)
        if self.type == "polars":
            polars = _import_polars()
            if payload.headers is not None:
                return polars.DataFrame(
                    [] if payload.data == [[]] else payload.data, schema=payload.headers
                )
            else:
                return polars.DataFrame(payload.data)
        if self.type == "numpy":
            return np.array(payload.data)
        elif self.type == "array":
            return payload.data  # type: ignore
        else:
            raise ValueError(
                "Unknown type: "
                + str(self.type)
                + ". Please choose from: 'pandas', 'numpy', 'array', 'polars'."
            )

    def postprocess(
        self,
        value: pd.DataFrame
        | Styler
        | np.ndarray
        | pl.DataFrame
        | list
        | list[list]
        | dict
        | str
        | None,
    ) -> DataframeData:
        """
        Parameters:
            value: Expects data any of these formats: `pandas.DataFrame`, `pandas.Styler`, `numpy.array`, `polars.DataFrame`, `list[list]`, `list`, or a `dict` with keys 'data' (and optionally 'headers'), or `str` path to a csv, which is rendered as the spreadsheet.
        Returns:
            the uploaded spreadsheet data as an object with `headers` and `data` attributes
        """
        import pandas as pd
        from pandas.io.formats.style import Styler

        if value is None:
            return self.postprocess(self.empty_input)
        if isinstance(value, dict):
            if len(value) == 0:
                return DataframeData(headers=self.headers, data=[[]])
            return DataframeData(
                headers=value.get("headers", []), data=value.get("data", [[]])
            )
        if isinstance(value, (str, pd.DataFrame)):
            if isinstance(value, str):
                value = pd.read_csv(value)  # type: ignore
            if len(value) == 0:
                return DataframeData(
                    headers=list(value.columns),  # type: ignore
                    data=[[]],  # type: ignore
                )
            return DataframeData(
                headers=list(value.columns),  # type: ignore
                data=value.to_dict(orient="split")["data"],  # type: ignore
            )
        elif isinstance(value, Styler):
            if semantic_version.Version(pd.__version__) < semantic_version.Version(
                "1.5.0"
            ):
                raise ValueError(
                    "Styler objects are only supported in pandas version 1.5.0 or higher. Please try: `pip install --upgrade pandas` to use this feature."
                )
            if self.interactive:
                warnings.warn(
                    "Cannot display Styler object in interactive mode. Will display as a regular pandas dataframe instead."
                )
            df: pd.DataFrame = value.data  # type: ignore
            if len(df) == 0:
                return DataframeData(
                    headers=list(df.columns),
                    data=[[]],
                    metadata=self.__extract_metadata(value),  # type: ignore
                )
            return DataframeData(
                headers=list(df.columns),
                data=df.to_dict(orient="split")["data"],  # type: ignore
                metadata=self.__extract_metadata(value),  # type: ignore
            )
        elif _is_polars_available() and isinstance(value, _import_polars().DataFrame):
            if len(value) == 0:
                return DataframeData(headers=list(value.to_dict().keys()), data=[[]])
            df_dict = value.to_dict()
            headers = list(df_dict.keys())
            data = list(zip(*df_dict.values()))
            return DataframeData(headers=headers, data=data)
        elif isinstance(value, (np.ndarray, list)):
            if len(value) == 0:
                return DataframeData(headers=self.headers, data=[[]])
            if isinstance(value, np.ndarray):
                value = value.tolist()
            if not isinstance(value, list):
                raise ValueError("output cannot be converted to list")

            _headers = self.headers
            if len(self.headers) < len(value[0]):
                _headers: list[str] = [
                    *self.headers,
                    *[str(i) for i in range(len(self.headers) + 1, len(value[0]) + 1)],
                ]
            elif len(self.headers) > len(value[0]):
                _headers = self.headers[: len(value[0])]

            return DataframeData(headers=_headers, data=value)
        else:
            raise ValueError("Cannot process value as a Dataframe")

    @staticmethod
    def __get_cell_style(cell_id: str, cell_styles: list[dict]) -> str:
        styles_for_cell = []
        for style in cell_styles:
            if cell_id in style.get("selectors", []):
                styles_for_cell.extend(style.get("props", []))
        styles_str = "; ".join([f"{prop}: {value}" for prop, value in styles_for_cell])
        return styles_str

    @staticmethod
    def __extract_metadata(df: Styler) -> dict[str, list[list]]:
        metadata = {"display_value": [], "styling": []}
        style_data = df._compute()._translate(None, None)  # type: ignore
        cell_styles = style_data.get("cellstyle", [])
        for i in range(len(style_data["body"])):
            metadata["display_value"].append([])
            metadata["styling"].append([])
            for j in range(len(style_data["body"][i])):
                cell_type = style_data["body"][i][j]["type"]
                if cell_type != "td":
                    continue
                display_value = style_data["body"][i][j]["display_value"]
                cell_id = style_data["body"][i][j]["id"]
                styles_str = Dataframe.__get_cell_style(cell_id, cell_styles)
                metadata["display_value"][i].append(display_value)
                metadata["styling"][i].append(styles_str)
        return metadata

    @staticmethod
    def __process_counts(count, default=3) -> tuple[int, str]:
        if count is None:
            return (default, "dynamic")
        if isinstance(count, (int, float)):
            return (int(count), "dynamic")
        else:
            return count

    @staticmethod
    def __validate_headers(headers: list[str] | None, col_count: int):
        if headers is not None and len(headers) != col_count:
            raise ValueError(
                f"The length of the headers list must be equal to the col_count int.\n"
                f"The column count is set to {col_count} but `headers` has {len(headers)} items. "
                f"Check the values passed to `col_count` and `headers`."
            )

    def process_example(
        self,
        value: pd.DataFrame
        | Styler
        | np.ndarray
        | pl.DataFrame
        | list
        | list[list]
        | dict
        | str
        | None,
    ):
        import pandas as pd

        if value is None:
            return ""
        value_df_data = self.postprocess(value)
        value_df = pd.DataFrame(value_df_data.data, columns=value_df_data.headers)
        return value_df.head(n=5).to_dict(orient="split")["data"]

    def example_payload(self) -> Any:
        return {"headers": ["a", "b"], "data": [["foo", "bar"]]}

    def example_value(self) -> Any:
        return {"headers": ["a", "b"], "data": [["foo", "bar"]]}

    
    def change(self,
        fn: Callable | None,
        inputs: Component | Sequence[Component] | set[Component] | None = None,
        outputs: Component | Sequence[Component] | None = None,
        api_name: str | None | Literal[False] = None,
        scroll_to_output: bool = False,
        show_progress: Literal["full", "minimal", "hidden"] = "full",
        queue: bool | None = None,
        batch: bool = False,
        max_batch_size: int = 4,
        preprocess: bool = True,
        postprocess: bool = True,
        cancels: dict[str, Any] | list[dict[str, Any]] | None = None,
        every: float | None = None,
        trigger_mode: Literal["once", "multiple", "always_last"] | None = None,
        js: str | None = None,
        concurrency_limit: int | None | Literal["default"] = "default",
        concurrency_id: str | None = None,
        show_api: bool = True) -> Dependency:
        """
        Parameters:
            fn: the function to call when this event is triggered. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
            inputs: List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
            outputs: List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
            api_name: Defines how the endpoint appears in the API docs. Can be a string, None, or False. If False, the endpoint will not be exposed in the api docs. If set to None, the endpoint will be exposed in the api docs as an unnamed endpoint, although this behavior will be changed in Gradio 4.0. If set to a string, the endpoint will be exposed in the api docs with the given name.
            scroll_to_output: If True, will scroll to output component on completion
            show_progress: If True, will show progress animation while pending
            queue: If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
            batch: If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then *required* to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
            max_batch_size: Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
            preprocess: If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
            postprocess: If False, will not run postprocessing of component data before returning 'fn' output to the browser.
            cancels: A list of other events to cancel when this listener is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
            every: Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds.
            trigger_mode: If "once" (default for all events except `.change()`) would not allow any submissions while an event is pending. If set to "multiple", unlimited submissions are allowed while pending, and "always_last" (default for `.change()` and `.key_up()` events) would allow a second submission after the pending event is complete.
            js: Optional frontend js method to run before running 'fn'. Input arguments for js method are values of 'inputs' and 'outputs', return should be a list of values for output components.
            concurrency_limit: If set, this is the maximum number of this event that can be running simultaneously. Can be set to None to mean no concurrency_limit (any number of this event can be running simultaneously). Set to "default" to use the default concurrency limit (defined by the `default_concurrency_limit` parameter in `Blocks.queue()`, which itself is 1 by default).
            concurrency_id: If set, this is the id of the concurrency group. Events with the same concurrency_id will be limited by the lowest set concurrency_limit.
            show_api: whether to show this event in the "view API" page of the Gradio app, or in the ".view_api()" method of the Gradio clients. Unlike setting api_name to False, setting show_api to False will still allow downstream apps as well as the Clients to use this event. If fn is None, show_api will automatically be set to False.
        """
        ...
    
    def input(self,
        fn: Callable | None,
        inputs: Component | Sequence[Component] | set[Component] | None = None,
        outputs: Component | Sequence[Component] | None = None,
        api_name: str | None | Literal[False] = None,
        scroll_to_output: bool = False,
        show_progress: Literal["full", "minimal", "hidden"] = "full",
        queue: bool | None = None,
        batch: bool = False,
        max_batch_size: int = 4,
        preprocess: bool = True,
        postprocess: bool = True,
        cancels: dict[str, Any] | list[dict[str, Any]] | None = None,
        every: float | None = None,
        trigger_mode: Literal["once", "multiple", "always_last"] | None = None,
        js: str | None = None,
        concurrency_limit: int | None | Literal["default"] = "default",
        concurrency_id: str | None = None,
        show_api: bool = True) -> Dependency:
        """
        Parameters:
            fn: the function to call when this event is triggered. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
            inputs: List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
            outputs: List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
            api_name: Defines how the endpoint appears in the API docs. Can be a string, None, or False. If False, the endpoint will not be exposed in the api docs. If set to None, the endpoint will be exposed in the api docs as an unnamed endpoint, although this behavior will be changed in Gradio 4.0. If set to a string, the endpoint will be exposed in the api docs with the given name.
            scroll_to_output: If True, will scroll to output component on completion
            show_progress: If True, will show progress animation while pending
            queue: If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
            batch: If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then *required* to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
            max_batch_size: Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
            preprocess: If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
            postprocess: If False, will not run postprocessing of component data before returning 'fn' output to the browser.
            cancels: A list of other events to cancel when this listener is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
            every: Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds.
            trigger_mode: If "once" (default for all events except `.change()`) would not allow any submissions while an event is pending. If set to "multiple", unlimited submissions are allowed while pending, and "always_last" (default for `.change()` and `.key_up()` events) would allow a second submission after the pending event is complete.
            js: Optional frontend js method to run before running 'fn'. Input arguments for js method are values of 'inputs' and 'outputs', return should be a list of values for output components.
            concurrency_limit: If set, this is the maximum number of this event that can be running simultaneously. Can be set to None to mean no concurrency_limit (any number of this event can be running simultaneously). Set to "default" to use the default concurrency limit (defined by the `default_concurrency_limit` parameter in `Blocks.queue()`, which itself is 1 by default).
            concurrency_id: If set, this is the id of the concurrency group. Events with the same concurrency_id will be limited by the lowest set concurrency_limit.
            show_api: whether to show this event in the "view API" page of the Gradio app, or in the ".view_api()" method of the Gradio clients. Unlike setting api_name to False, setting show_api to False will still allow downstream apps as well as the Clients to use this event. If fn is None, show_api will automatically be set to False.
        """
        ...
    
    def select(self,
        fn: Callable | None,
        inputs: Component | Sequence[Component] | set[Component] | None = None,
        outputs: Component | Sequence[Component] | None = None,
        api_name: str | None | Literal[False] = None,
        scroll_to_output: bool = False,
        show_progress: Literal["full", "minimal", "hidden"] = "full",
        queue: bool | None = None,
        batch: bool = False,
        max_batch_size: int = 4,
        preprocess: bool = True,
        postprocess: bool = True,
        cancels: dict[str, Any] | list[dict[str, Any]] | None = None,
        every: float | None = None,
        trigger_mode: Literal["once", "multiple", "always_last"] | None = None,
        js: str | None = None,
        concurrency_limit: int | None | Literal["default"] = "default",
        concurrency_id: str | None = None,
        show_api: bool = True) -> Dependency:
        """
        Parameters:
            fn: the function to call when this event is triggered. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
            inputs: List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
            outputs: List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
            api_name: Defines how the endpoint appears in the API docs. Can be a string, None, or False. If False, the endpoint will not be exposed in the api docs. If set to None, the endpoint will be exposed in the api docs as an unnamed endpoint, although this behavior will be changed in Gradio 4.0. If set to a string, the endpoint will be exposed in the api docs with the given name.
            scroll_to_output: If True, will scroll to output component on completion
            show_progress: If True, will show progress animation while pending
            queue: If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
            batch: If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then *required* to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
            max_batch_size: Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
            preprocess: If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
            postprocess: If False, will not run postprocessing of component data before returning 'fn' output to the browser.
            cancels: A list of other events to cancel when this listener is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
            every: Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds.
            trigger_mode: If "once" (default for all events except `.change()`) would not allow any submissions while an event is pending. If set to "multiple", unlimited submissions are allowed while pending, and "always_last" (default for `.change()` and `.key_up()` events) would allow a second submission after the pending event is complete.
            js: Optional frontend js method to run before running 'fn'. Input arguments for js method are values of 'inputs' and 'outputs', return should be a list of values for output components.
            concurrency_limit: If set, this is the maximum number of this event that can be running simultaneously. Can be set to None to mean no concurrency_limit (any number of this event can be running simultaneously). Set to "default" to use the default concurrency limit (defined by the `default_concurrency_limit` parameter in `Blocks.queue()`, which itself is 1 by default).
            concurrency_id: If set, this is the id of the concurrency group. Events with the same concurrency_id will be limited by the lowest set concurrency_limit.
            show_api: whether to show this event in the "view API" page of the Gradio app, or in the ".view_api()" method of the Gradio clients. Unlike setting api_name to False, setting show_api to False will still allow downstream apps as well as the Clients to use this event. If fn is None, show_api will automatically be set to False.
        """
        ...